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Content analysis from user's relevance feedback for content-based image retrieval
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Wei, C. H. and Li, Chang-Tsun (2009) Content analysis from user's relevance feedback for content-based image retrieval. In: Ma, Zongmin, (ed.) Artificial Intelligence for Maximizing Content Based Image Retrieval. IGI Global, pp. 216-234. ISBN 9781605661759
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Official URL: http://dx.doi.org/10.4018/978-1-60566-174-2.ch010
Abstract
An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user’s knowledge, cultural background, personal feelings and so on. Content-based image retrieval (CBIR) systems must be able to interact with users and discover the current user’s information needs. An interactive search paradigm that has been developed for image retrieval is machine learning with a user-in-the-loop, guided by relevance feedback, which refers to the notion of relevance of the individual image based on the current user’s subjective judgment. Relevance feedback serves as an information carrier to convey the user’s information needs / preferences to the retrieval system. This chapter not only provides the fundamentals of CBIR systems and relevance feedback for understanding and incorporating relevance feedback into CBIR systems, but also discusses several approaches to analyzing and learning relevance feedback.
Item Type: | Book Item | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Publisher: | IGI Global | ||||
ISBN: | 9781605661759 | ||||
Book Title: | Artificial Intelligence for Maximizing Content Based Image Retrieval | ||||
Editor: | Ma, Zongmin | ||||
Official Date: | 2009 | ||||
Dates: |
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Number of Pages: | 19 | ||||
Page Range: | pp. 216-234 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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